EY EY-Parthenon - Strategy Execution Growth 

United States, New York, New York 

40006654

With deep functional and sector expertise, paired with innovative AI-powered technology and an investor mindset, we partner with CEOs, Boards, Private Equity and Governments every step of the way – enabling you to shape your future with confidence.

Your key responsibilities

· Lead ingestion and ETL design for structured and semi-structured data (CSV, JSON, APIs, Flat Files).

· Understand schema, data quality, and transformation logic for multiple sources on a client-by-client like NAIC, NOAA, Google Trends, EBRI, Cannex, LIMRA, and internal client logs.

· Design normalization and joining pipelines across vertical domains (insurance + consumer + economic data).

· Build data access layers optimized for ML (feature stores, event streams, vector stores).

· Define and enforce standards for data provenance, quality checks, logging, and version control.

· Partner with AI/ML and Platform teams to ensure data is ML- and privacy-ready (HIPAA, SOC2, etc.).

· Communicate findings and recommendations to stakeholders through compelling data storytelling and visualizations.

· Lead the design, development, and deployment of AI and machine learning models and statistical analyses.

· Translate complex business problems into data science solutions using advanced analytics techniques.

To qualify for the role, you must have

· A bachelor’s degree in Business, Statistics, Economics, Mathematics, Engineering, Computer Science, Analytics, or other related field and 8 years of related work experience; or a graduate degree and approximately 7 years of related work experience.

· Proven experience in managing and developing high-performing data science and data engineering teams.

· Experience in data engineering or hybrid data science roles focused on pipeline scalability and schema management.

· Expertise in cloud-native data infrastructure (e.g., GCP/AWS, Snowflake, BigQuery, Databricks, Delta Lake).

· Strong SQL/Python/Scala proficiency and experience with orchestration tools (Airflow, dbt).

· Experience with merging and reconciling third-party data (public APIs, vendor flat files, dashboards).

· Comfort defining semantic layers and mapping unstructured/dirty datasets into usable models for AI/BI use.

· Basic understanding of ML/feature pipelines and downstream modeling needs.

· The ability and willingness to travel and work in excess of standard hours when necessary.

Ideally, you’ll also have

· Experience working in a startup and/or management/strategy consulting.

· Knowledge of how to leverage AI tools in a business setting, including Microsoft Copilot.

· Collaborative, problem-solving, and growth-oriented mindset.